Current engine trend data analysis techniques used by commercial airlines and the military rely, in general, on visual examination of trend data to detect symptoms indicative of the need for inspection or maintenance. This approach is labor intensive, and requires considerable experience in order to identify real trend changes from "rogue" data and sensor shifts. Consequently, much of the value of trending can be lost unless a user is dedicated to frequent detailed analysis of engine trend data. While large commercial airlines are dedicated to trend monitoring, smaller airlines and other commercial operators, as well as the United States military, do not take full advantage of trend monitoring because of dedication required to analyzing data as well as the uncertainty in how to interpret trend symptoms for maintenance.
In the existing art, the United States Air Force utilizes a statistical analysis technique to perform automatic detection of trend shifts by computation of a slope with a threshold in order to trigger an "alarm". Although this method of trend detection works well, in some cases small changes may be missed or may take several fights before the trend change can be identified. This, of course, delays detection of a potentially critical situation. In addition, this technique is not able to discern scatter and sensor drift.backslash.shift from real engine changes. Consequently, considerable experience is still required to interpret detected symptoms. This has resulted in diminished use of the trend information as a maintenance driver.
It would be desirable, then, to have an engine trend data analysis technique which provides increased efficiency in detecting anomalous trends, and which is also less labor intensive. The objects, features and advantages of the present invention will become more readily apparent in the following description when taken in conjunction with the appended drawings.